一种铝灰处理物料运载与烟尘处理方法及系统
By combining multi-source sensing devices, edge-side collaborative computing, and intelligent control algorithms, the problems of poor data quality and control disconnect in the aluminum ash calcination system have been solved, achieving high-precision real-time monitoring and ultra-low emissions in the aluminum ash calcination process, and improving system stability and energy efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HUBEI YUCHEN NEW MATERIALS TECHNOLOGY CO LTD
- Filing Date
- 2026-04-13
- Publication Date
- 2026-07-17
AI Technical Summary
Existing high-temperature calcination systems for aluminum ash suffer from poor data quality, weak operational condition sensing capabilities, disconnect between material conveying and dust purification control, and a lack of dynamic prediction and adaptive optimization mechanisms, making it difficult to achieve coordinated management and control of stable operation and ultra-low emissions.
The entire process of aluminum ash roasting is monitored in real time by multi-source sensing devices. Wavelet threshold denoising and cubic spline interpolation algorithms are used for cleaning and reconstruction. End-side collaborative computing analysis is used to generate fully enclosed operating condition coding information. Long short-term memory network model is combined to predict the load change trend of the dust purification system. The material conveying rate and induced draft parameters are optimized by deep deterministic strategy gradient algorithm. Finally, the frequency conversion control command is issued in real time through programmable logic controller and industrial Ethernet.
It achieves high-precision and robust real-time monitoring of the entire aluminum ash calcination process, early identification of abnormal operating conditions, accurate prediction of load changes in the flue gas purification system, and dynamic adjustment of control parameters, resulting in a significant improvement in system stability, energy utilization efficiency, and ultra-low emission levels.
Smart Images
Figure CN122012933B_ABST